Tal Arbel

Biography

Tal Arbel is a professor in the Department of Electrical and Computer Engineering at McGill University, where she is the director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines.

She is also a Canada CIFAR AI Chair, an associate academic member of Mila – Quebec Artificial Intelligence Institute and an associate member of the Goodman Cancer Research Centre.

Arbel’s research focuses on the development of probabilistic deep learning methods in computer vision and medical image analysis for a wide range of real-world applications, with a focus on neurological diseases.

She is a recipient of the 2019 McGill Engineering Christophe Pierre Research Award and a Fellow of the Canadian Academy of Engineering. She regularly serves on the organizing team of major international conferences in computer vision and in medical image analysis (e.g. MICCAI, MIDL, ICCV, CVPR). She is currently the Editor-in-Chief and co-founder of the arXiv overlay journal: Machine Learning for Biomedical Imaging (MELBA).

Current Students

Berardino Barile

Postdoctorate - McGill University

Joshua Durso-finley

PhD - McGill University

Nima Fathi

Master's Research - McGill University

Gian Favero

Master's Research - McGill University

elizabeth.janes@mila.quebec

Ethan Honey

PhD - McGill University

ethan.honey@mila.quebec

Elizabeth Laura Janes

Master's Research - McGill University

Emily Kaczmarek

PhD - McGill University

emily.kaczmarek@mail.mcgill.ca

Amar Kumar

PhD - McGill University

Mohamed Mohamed

Master's Research - McGill University

Parham Saremi

Master's Research - McGill University

Xing Shen

Master's Research - McGill University

Master's Research - McGill University

Zahra TehraniNasab

Master's Research - McGill University

Mona Wang Wang

Undergraduate - McGill University

monaw7777@gmail.com

Ritchie Yu

Undergraduate - McGill University

Publications

How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz

Peter M. Full

Hrvoje Bogunovic

Bennett Landman

Oskar Maier

Bjoern Menze

Gregory C. Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

F. V. D. Sommen

Guoyan Zheng

Henning Müller

Michal Kozubek

Andrew P. Bradley

Pierre Jannin … (see 2 more)

Annette Kopp-Schneider

Lena Maier-Hein

2018-09-13

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (published)

RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours

Raghav Mehta

2018-09-12

Simulation and Synthesis in Medical Imaging (published)

arxiv.org

Structured deep Fisher pruning for efficient facial trait classification

Qing Tian

James J. Clark

2018-09-01

Image and Vision Computing (published)

Fisher Pruning of Deep Nets for Facial Trait Classification

Qing Tian

James J. Clark

Although deep nets have resulted in high accuracies for various visual tasks, their computational and space requirements are prohibitively h… (see more)igh for inclusion on devices without high-end GPUs. In this paper, we introduce a neuron/filter level pruning framework based on Fisher's LDA which leads to high accuracies for a wide array of facial trait classification tasks, while significantly reducing space/computational complexities. The approach is general and can be applied to convolutional, fully-connected, and module-based deep structures, in all cases leveraging the high decorrelation of neuron activations found in the pre-decision layer and cross-layer deconv dependency. Experimental results on binary and multi-category facial traits from the LFWA and Adience datasets illustrate the framework's comparable/better performance to state-of-the-art pruning approaches and compact structures (e.g. SqueezeNet, MobileNet). Ours successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG-16, 82% for GoogLeNet) and with significant FLOP reductions (83% for VGG-16, 64% for GoogLeNet).

2018-03-21

ArXiv (preprint)

arxiv.org

Task-specific Deep LDA pruning of neural networks

Qing Tian

James J. Clark

With deep learning's success, a limited number of popular deep nets have been widely adopted for various vision tasks. However, this usually… (see more) results in unnecessarily high complexities and possibly many features of low task utility. In this paper, we address this problem by introducing a task-dependent deep pruning framework based on Fisher's Linear Discriminant Analysis (LDA). The approach can be applied to convolutional, fully-connected, and module-based deep network structures, in all cases leveraging the high decorrelation of neuron motifs found in the pre-decision layer and cross-layer deconv dependency. Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks. Experimental results on datasets of generic objects, as well as domain specific tasks (CIFAR100, Adience, and LFWA) illustrate our framework's superior performance over state-of-the-art pruning approaches and fixed compact nets (e.g. SqueezeNet, MobileNet). The proposed method successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG16, up to 82% for the already compact InceptionNet) and with significant FLOP reductions (83% for VGG16, up to 64% for InceptionNet). Through pruning, we can also derive smaller, but more accurate and more robust models suitable for the task.

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases

Ian J. Gerard

Marta Kersten-Oertel

Simon Drouin

Jeffery A. Hall

Kevin Petrecca

Dante De Nigris

Daniel A. Di Giovanni

D. Louis Collins

Abstract. We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented … (see more)reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient’s cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.

2018-01-26

Journal of Medical Imaging (published)

Challenging Conventional Segmentation Evaluation Metrics Focal Pathology ( Lesion and Tumour ) Segmentation from Patient Images

How can we do better ? Pitfalls in biomedical challenge design and how to address them

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz

Hrvoje Bogunovic

Andrew P. Bradley

Aaron

Carass

Carolin Feldmann

Alejandro F. Frangi

Peter M. Full

Bram Ginneken Van

Ginneken

Allan Hanbury

Katrin Honauer

Michal Kozubek

Adam Bennett

Landman … (see 22 more)

Keno März

Oskar Maier

Klaus Maier-Hein

Bjoern Menze

Henning Müller

Peter F. Neher

Wiro Niessen

NASIR RAJPOOT

Catherine Gregory

Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

Christian Stock

Danail

Stoyanov

Abdel Aziz Taha

F. V. D. Sommen

Ching-Wei Wang

Marc-André Weber

Guoyan Zheng

Pierre Jannin

Lena Maier-Hein

Since the first MICCAI grand challenge was organized in 2007 [1], the impact of biomedical image analysis challenges on both the research fi… (see more)eld as well as on individual careers has been steadily growing. For example, the acceptance of a journal article today often depends on the performance of a new algorithm being assessed against the state-ofthe-art work on publicly available challenge datasets. Furthermore, the results are also important for the individuals scientific careers as well as the potential that algorithms can be translated into clinical practice. Yet, while the publication of papers in scientific journals and prestigious conferences, such as MICCAI, undergoes strict quality control, the design and organization of challenges do not. To investigate the effect of common practice, we have formed an international initiative dedicated to analyzing and improving a variety of aspects related to biomedical challenge design, execution and reporting [2]. In the first part of our abstract presentation at LABELS workshop, we are going to present some of the major pitfalls related to biomedical image analysis challenges today. Specifically, we will look at the following research questions: RQ1: How robust are challenge rankings? What is the effect of – the specific test cases used? – the specific metric variant(s) applied? – the rank aggregation method chosen (e.g. aggregation of metric values with the mean vs median)? ? Shared first/senior authors.

Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients Using a Bag-of-Lesions Brain Representation

Andrew Doyle

Doina Precup

Douglas Arnold

2017-09-04

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 (published)

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

M. Cardoso

Enzo Ferrante

Xavier Pennec

Adrian Dalca

Sarah Parisot

S. Joshi

Nematollah Batmanghelich

Aristeidis Sotiras

Mads Lenstrup Nielsen

M. Sabuncu

Tom Fletcher

Li Shen

Stanley Durrleman

Stefan H. Sommer

2017-09-01

Lecture Notes in Computer Science (published)

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

M. Cardoso

G. Carneiro

T. Syeda-Mahmood

J. Tavares

Mehdi Moradi

Andrew P. Bradley

Hayit Greenspan

J. Papa

Anant. Madabhushi

Jacinto C Nascimento

Jaime S. Cardoso

Vasileios Belagiannis

Zhi Lu

Faculdade Engenharia

2017-07-19

ArXiv (preprint)

arxiv.org

Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures

M. Cardoso

Xiongbiao Luo

Stefan Wesarg

Tobias Reichl

M. Ballester

Jonathan Mcleod

Klaus Dr. Drechsler

T. Peters

Marius Erdt

Kensaku Mori

M. Linguraru

Andreas Uhl

Cristina Oyarzun Laura

R. Shekhar

2017-01-01

Lecture Notes in Computer Science (published)